ArticleNature methods2024
Mapping cellular interactions from spatially resolved transcriptomics data.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed.
- Single-cell and spatial RNA sequencing in prostate cancer.Nature reviews. Urology · 2026Review
- HIGH-DIMENSIONAL MULTI-OMIC MAPPING OF POST-MORTEM HUMAN BRAIN USING ITERATIVE INDIRECT IMMUNOFLUORESCENCE IMAGING ON XENIUM-PROCESSED TISSUES.bioRxiv : the preprint server for biology · 2026Article
- Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.Briefings in bioinformatics · 2026Review
- Spatial ecotype in tumor immune exclusion: from spatial architecture to therapeutic strategies.Molecular cancer · 2026Review
- Mapping and engineering the human cell-cell interactome.Nature biotechnology · 2026Review
- Inference of secreted protein signaling activities in intercellular communication.Nature methods · 2026Article
- Macrophage signaling associates with fibrogenic program activation in periductal fibroblasts in pediatric primary sclerosing cholangitis.JCI insight · 2026Article
- Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods.Briefings in bioinformatics · 2026Review
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Glycosaminoglycans in tissue regeneration: Insights into glycobiology and their biomedical application.Bioactive materials · 2026Review
- GraphTME: graph-based framework for predicting immunotherapy response by interpreting tumour microenvironment interactions using spatial transcriptomics.NPJ systems biology and applications · 2026Article
- InSituPREP enables 3D single-cell mapping of interaction-associated gene programs in the breast cancer tumor microenvironment.Nucleic acids research · 2026Article
- FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.Nature communications · 2026Article
- Artificial intelligence-assisted spatial omics-based biomimetic nanoplatform for intelligent and precise intervention in the immunosuppressive core region of ovarian cancer.NPJ precision oncology · 2026Review
- Spatially Resolved Multiomics Reveals Metabolic Remodeling and Autophagy Activation in Adamantinomatous Craniopharyngiomas.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Single-cell omics in tumor lymph node metastasis: mechanisms and therapeutic implications.Molecular cancer · 2026Review
- Intra-tumoural microenvironment and bugs-based drug design: foreseeable future in oncology and immuno-oncology.Frontiers in pharmacology · 2026Review
- Transmembrane lectins in cancer immunity: emerging drivers of myeloid-mediated immunosuppression.Frontiers in immunology · 2026Review
- Cell-state transitions and microenvironmental remodeling in thyroid cancer progression revealed by single-cell and spatial transcriptomics.Frontiers in immunology · 2026Review
- Single cell spatial transcriptomics integration deciphers the morphological heterogeneity of atherosclerotic carotid arteries.Nature communications · 2025Article
Corrections and comments
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Authors and funding
25 authors.
Funding
Abstract
Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.
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39227721What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.